Dean Truong
Research skills
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TechnicalLanguages: C/C++, Java, Python, Perl, TCL, Verilog, MIPS/RISC-based assembly., CAD Tools: MATLAB, Hspice, Nanosim, Virtuoso, SOC Encounter, Design Compiler, ModelSim, NC Verilog, Simvision, Module Compiler, Calibre, StarRCXT, Primetime, Eclipse, g++/gcc, ARM Keil Dev. Tools., Microcontrollers: 68HC812A4 and MC9S12E128 (Freescale/Motorola, Stellaris LM3S8962 (TI/ARM).
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ITBasic Linux usage and administration. Proficient at both Mac OS X and Windows.
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StatisticalMy publication statistics thus far, Papers: 15, Cites/paper: 5.20, h-index: 5, AWCR: 16.17, Citations: 78, Cites/author: 17.31, g-index: 8, AW-index: 4.02, Years: 4, Papers/author: 4.23, hc-index: 7, AWCRpA: 4.28, Cites/year: 19.50, Authors/paper: 4.07, hI-index: 0.96, e-index: 5.39, norm: 2, hm-index: 2.19, Hirsch a=3.12, m=1.25, Contemporary ac=1.33, Cites/paper 5.20/4.0/5 (mean/median/mode, Authors/paper 4.07/3.0/3 (mean/median/mode, 1 paper(s) with 2 author(s, 8 paper(s) with 3 author(s, 3 paper(s) with 5 author(s, 1 paper(s) with 6 author(s, 2 paper(s) with 7 author(s
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OtherPublishing: LaTeX, Visio, Word, Powerpoint, Acrobat, GIMP.
Research interests
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InterestsBiomedical Engineering, Hierarchical Control Systems, Many-core DSP, Energy-efficient DVFS, Perceptual Control Theory
Research experience
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Jun 2010–
Jul 2011Research: Dynamic Voltage and Frequency Scaling for Energy Efficient Processing
University of California at Davis · Electrical and Computer Engineering · University of California, DavisVLSI Computation Laboratory · DavisGlobally Asynchronous Locally Synchronous, fine-grain DVFS, software controlled DVFS, non-linear control systems modeling -
Jan 2009–
May 2010Research: Laboratory Manager
University of California at Davis · Electrical and Computer Engineering · University of California, DavisVLSI Computation Laboratory · Davis"Publish or Perish" -
Sep 2006–
Dec 2008Research: 167-core Computational Platform
University of California at Davis · Electrical and Computer Engineering · University of California, DavisVLSI Computation Laboratory · Davismany-core, multi-core, DSP, baseband communication processing, video processing
Education
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Sep 2010–
Jul 2011University of California at Davis
Electrical and Computer Engineering · Ph.D.United States of America (USA) · Davis -
Sep 2007
University of California, Davis
Seminar on College TeachingUnited States of America (USA) · Davis -
Sep 2005–
Jul 2010University of California at Davis
Eletrical and Computer Engineering · M.S.United States of America (USA) · Davis -
Sep 2001–
Jul 2005University of California at Davis
Electrical and Computer Engineering · B.S.United States of America (USA) · Davis
Other
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LanguagesEnglish! (American Variant)
Publications
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A Reconfigurable Source-Synchronous On-Chip Network for GALS Many-Core Platforms
Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on. 06/2010; 29:897 -910.
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A Low-Complexity Message-Passing Algorithm for Reduced Routing Congestion in LDPC Decoders
Circuits and Systems I: Regular Papers, IEEE Transactions on. 05/2010; 57:1048 -1061.
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An Improved Split-Row Threshold Decoding Algorithm for LDPC Codes
Communications, 2009. ICC '09. IEEE International Conference on; 07/2009
We present an improved thresholding LDPC decoding algorithm which outperforms the split-row and original split-row threshold decoders with a small increase in hardware. Simulation results show that the algorithm provides 0.27- 0.50 dB coding gain over split-row, 0.10-0.20 dB over split-row threshold... [more] We present an improved thresholding LDPC decoding algorithm which outperforms the split-row and original split-row threshold decoders with a small increase in hardware. Simulation results show that the algorithm provides 0.27- 0.50 dB coding gain over split-row, 0.10-0.20 dB over split-row threshold, and is within 0.08-0.13 dB of SPA. Compared with the original threshold algorithm the check node processor's gate count is increased by 3% while total chip area is kept the same.
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Multi-Split-Row Threshold decoding implementations for LDPC codes
Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on; 06/2009
The recently introduced Split-Row Threshold algorithm significantly improves the error performance when compared to the non- threshold Split-Row algorithm while requiring a very small increase in hardware complexity. The Multi-Split-Row Threshold decoding algorithm presented in this paper enables fu... [more] The recently introduced Split-Row Threshold algorithm significantly improves the error performance when compared to the non- threshold Split-Row algorithm while requiring a very small increase in hardware complexity. The Multi-Split-Row Threshold decoding algorithm presented in this paper enables further reductions in routing complexity for greater throughput and smaller circuit area implementations. Several Multi-Split-Row Threshold decoder designs have been implemented in 65 nm CMOS and the impact of the different levels of partitioning on error performance, wire interconnect complexity, decoder area, and speed are investigated. The Split-Row-16 Threshold decoder occupies 3.8 mm<sup>2</sup>, runs at 100 MHz, delivers a throughput of 13.8 Gbps at 15 iterations and is only 0.28 dB and 0.22 dB away from SPA and MinSum Normalized.
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A 167-Processor Computational Platform in 65 nm CMOS
Solid-State Circuits, IEEE Journal of. 04/2009; 44:1130 -1144.
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A GALS many-core heterogeneous DSP platform with source-synchronous on-chip interconnection network.
Third International Symposium on Networks-on-Chips, NOCS 2009, May 10-13 2009, La Jolla, CA, USA. Proceedings; 01/2009
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A 167-processor 65 nm computational platform with per-processor dynamic supply voltage and dynamic clock frequency scaling
VLSI Circuits, 2008 IEEE Symposium on; 07/2008
A 167-processor 65 nm computational platform well suited for DSP, communication, and multimedia workloads contains 164 programmable processors with dynamic supply voltage and dynamic clock frequency circuits, three algorithm-specific processors, and three 16 KB shared memories, all clocked by indepe... [more] A 167-processor 65 nm computational platform well suited for DSP, communication, and multimedia workloads contains 164 programmable processors with dynamic supply voltage and dynamic clock frequency circuits, three algorithm-specific processors, and three 16 KB shared memories, all clocked by independent oscillators and connected by configurable long-distance-capable links.
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3.15Impact points
AsAP: An Asynchronous Array of Simple Processors
Solid-State Circuits, IEEE Journal of. 04/2008;
An array of simple programmable processors is implemented in 0.18 mum CMOS and contains 36 asynchronously clocked independent processors. Each processor occupies 0.66 and is fully functional at a clock rate of 520-540 MHz at 1.8 V and over 600 MHz at 2.0 V. Processors dissipate an average of 32 mW u... [more] An array of simple programmable processors is implemented in 0.18 mum CMOS and contains 36 asynchronously clocked independent processors. Each processor occupies 0.66 and is fully functional at a clock rate of 520-540 MHz at 1.8 V and over 600 MHz at 2.0 V. Processors dissipate an average of 32 mW under typical conditions at 1.8 V and 475 MHz, and 2.4 mW at 0.9 V and 116 MHz while executing applications such as a JPEG encoder core and a fully compliant IEEE 802.11 a/g wireless LAN baseband transmitter.
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3.21Impact points
AsAP: A Fine-Grained Many-Core Platform for DSP Applications
Micro, IEEE. 04/2007;
Many emerging and future applications require significant levels of complex digital signal processing and operate within limited power budgets. Moreover, dramatically rising VLSI fabrication and design costs make programmable and reconfigurable solutions increasingly attractive. the ASAP project add... [more] Many emerging and future applications require significant levels of complex digital signal processing and operate within limited power budgets. Moreover, dramatically rising VLSI fabrication and design costs make programmable and reconfigurable solutions increasingly attractive. the ASAP project addresses these challenges with a chip multiprocessor composed of simple processors with small memories, achieving high energy efficiency and throughput in a small chip area.
Following (19)
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Remi Mollicone
CFAR-m -
David Geisler
Massachusetts Institute of Technology -
Anh Tran
University of California at Davis -
Ryan Scott
University of California at Davis -
Roberto Proietti
University of California at Davis